Finally, this article aims to highlight the
potential of time series and regression implicit
copula processes.
In machine learning they offer a computationally convenient avenue to extend existing deep models
to allow for uncertainty quantification. Examples include
[1]} who do so using Gaussian copula processes
and [2]} who using the regression copulas in Section  with deep basis functions.
The state space
copula proposed by [3]} and outlined in Section REF  also has substantial
potential. Many existing statistical and econometric models can be
written in state space form, from simple time series models to smoothing splines.
Combining their implicit copulas with flexible marginals
extends these models to more complex data distributions in a
straightforward fashion.
